The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Motion blur is a critical artefact frequently found in analogical or digital photos. For static or quasi-static scenes, motion blur is typically global and it is due to a noticeable handheld camera motion (also called “jitter”) during the acquisition of a picture/frame (e.g. still picture/photo, “video frame”) by a user and particularly during a photo exposure time. The degree of motion blur in a picture depends on several factors, as for example camera speed and trajectory, which are themselves dependent on the user skill or situation (walking or sitting in a car for example), on camera settings such as focal length, on the sensor pixel pitch or on the camera weight. Furthermore, moving objects can also induce local motion blur into the picture. Blur results in a significant decrease of the quality of photo and/or video acquisition. Detecting or estimating blur may allow compensating for it, so as to (at least partially) cancel it.
Conventional blur detection/estimation methods are often based on single-frame processing and are thus a posteriori methods which estimate (global or local) picture sharpness via high-pass image filters.
Another way to perform global blur estimation may rely on mechanical devices such as gyroscopes or accelerometers. However, these devices are generally coupled to high-end motion blur compensation techniques (based on optic group or sensor displacement), which are expensive and thus not acceptable for cheaper acquisition devices such as e.g. the majority of phone cameras.
Multi-frames solutions comprise initially monitoring a camera displacement to acquire video or multiple photos, to obtain camera displacement information by jitter extraction on the acquired video and then applying a scale factor to convert the blur trajectory from a video resolution to a photo resolution. However, accuracy of such techniques depends on spatial and temporal resolution of the acquired video. More precisely, these techniques generally rely on the assumption that a temporal interval between the acquired frames is held constant. Moreover, to perform accurate blur estimation for an exposure time, these techniques generally require a very high frame rate in camera displacement monitored by jitter extraction, which is typically cumbersome (if not impossible) to achieve in standard cameras or phone cameras.
For example, the document EP1117251A1 discloses a method for stabilizing a moving image formed using a sequence of successive frames which includes calculating a motion vector field between adjacent frames.
Embodiments of the invention improve the accuracy of detection of blurriness. Thus, acquiring sharpness of a resulting picture is facilitated.